OpenRaiser/Intern-Atlas huggingface dataset:
Psycho-Demographic Analysis of the Facebook Rainbow Campaign
Venue: arXiv cs.SI 2016
Year: 2016

Over the past decade, online social media has had a tremendous impact on the way people engage in social activism. For instance, about 26M Facebook users expressed their support in upholding the cause of marriage equality by overlaying their profile pictures with rainbow-colored filters. Similarly, hundreds of thousands of users changed their profile pictures to a black dot condemning incidents of sexual violence in India. This act of demonstrating support for social causes by changing online profile pictures is being referred to as pictivism. In this paper, we analyze the psycho-demographic profiles, social networking behavior, and personal interests of users who participated in the Facebook Rainbow campaign. Our study is based on a sample of about 800K detailed profiles of Facebook users combining questionnaire-based psychological scores with Facebook profile data. Our analysis provides detailed insights into psycho-demographic profiles of the campaign participants. We found that personality traits such as openness and neuroticism are both positively associated with the likelihood of supporting the campaign, while conscientiousness exhibited a negative correlation. We also observed that females, religious disbelievers, democrats and adults in the age group of 20 to 30 years are more likely to be a part of the campaign. Our research further confirms the findings of several previous studies which suggest that a user is more likely to participate in an online campaign if a large fraction of his/her friends are already doing so. We also developed machine learning models for predicting campaign participation. Users' personal interests, approximated by Facebook user like activity, turned out to be the best indicator of campaign participation. Our results demonstrated that a predictive model which leverages the aforementioned features accurately identifies campaign participants (AUC=0.76).
Predict Moves
Venue: arXiv cs.HC 2016
Year: 2016

Mobile applications and on-body devices are becoming increasingly ubiquitous tools for physical activity tracking. We propose utilizing a self-tracker's habits to support continuous prediction of whether they will reach their daily step goal, thus enabling a variety of potential persuasive interventions. Our aim is to improve the prediction by leveraging historical data and other qualitative (motivation for using the systems, location, gender) and, quantitative (age) features. We have collected datasets from two activity tracking platforms (Moves and Fitbit) and aim to check if the model we derive from one is generalizable over the other. In the following paper we establish a pipeline for extracting the data and formatting it for modeling. We discuss the approach we took and our findings while selecting the features and classification models for the dataset. We further discuss the notion of generalizability of the model across different types of dataset and the probable inclusion of non standard features to further improve the model's accuracy.
Online Contrastive Divergence with Generative Replay: Experience Replay without Storing Data
Venue: arXiv cs.LG 2016
Year: 2016

Conceived in the early 1990s, Experience Replay (ER) has been shown to be a successful mechanism to allow online learning algorithms to reuse past experiences. Traditionally, ER can be applied to all machine learning paradigms (i.e., unsupervised, supervised, and reinforcement learning). Recently, ER has contributed to improving the performance of deep reinforcement learning. Yet, its application to many practical settings is still limited by the memory requirements of ER, necessary to explicitly store previous observations. To remedy this issue, we explore a novel approach, Online Contrastive Divergence with Generative Replay (OCD_GR), which uses the generative capability of Restricted Boltzmann Machines (RBMs) instead of recorded past experiences. The RBM is trained online, and does not require the system to store any of the observed data points. We compare OCD_GR to ER on 9 real-world datasets, considering a worst-case scenario (data points arriving in sorted order) as well as a more realistic one (sequential random-order data points). Our results show that in 64.28% of the cases OCD_GR outperforms ER and in the remaining 35.72% it has an almost equal performance, while having a considerably reduced space complexity (i.e., memory usage) at a comparable time complexity.
Master's Thesis : Deep Learning for Visual Recognition
Venue: arXiv cs.CV 2016
Year: 2016

The goal of our research is to develop methods advancing automatic visual recognition. In order to predict the unique or multiple labels associated to an image, we study different kind of Deep Neural Networks architectures and methods for supervised features learning. We first draw up a state-of-the-art review of the Convolutional Neural Networks aiming to understand the history behind this family of statistical models, the limit of modern architectures and the novel techniques currently used to train deep CNNs. The originality of our work lies in our approach focusing on tasks with a low amount of data. We introduce different models and techniques to achieve the best accuracy on several kind of datasets, such as a medium dataset of food recipes (100k images) for building a web API, or a small dataset of satellite images (6,000) for the DSG online challenge that we've won. We also draw up the state-of-the-art in Weakly Supervised Learning, introducing different kind of CNNs able to localize regions of interest. Our last contribution is a framework, build on top of Torch7, for training and testing deep models on any visual recognition tasks and on datasets of any scale.
The constant of recognizability is computable for primitive morphisms
Venue: arXiv cs.DM 2016
Year: 2016

Mossé proved that primitive morphisms are recognizable. In this paper we give a computable upper bound for the constant of recognizability of such a morphism. This bound can be expressed only using the cardinality of the alphabet and the length of the longest image under the morphism of a letter.
Distributed Computation of Mixing Time
Venue: arXiv cs.DC 2016
Year: 2016

The mixing time of a graph is an important metric, which is not only useful in analyzing connectivity and expansion properties of the network, but also serves as a key parameter in designing efficient algorithms. We present an efficient distributed algorithm for computing the mixing time of undirected graphs. Our algorithm estimates the mixing time $τ_s$ (with respect to a source node $s$) of any $n$-node undirected graph in $O(τ_s \log n)$ rounds. Our algorithm is based on random walks and require very little memory and use lightweight local computations, and work in the CONGEST model. Hence our algorithm is scalable under bandwidth constraints and can be an helpful building block in the design of topologically aware networks.
Packet Error Rate Analysis of Uncoded Schemes in Block-Fading Channels using Extreme Value Theory
Venue: arXiv cs.IT 2016
Year: 2016

We present a generic approximation of the packet error rate (PER) function of uncoded schemes in the AWGN channel using extreme value theory (EVT). The PER function can assume both the exponential and the Gaussian Q-function bit error rate (BER) forms. The EVT approach leads us to a best closed-form approximation, in terms of accuracy and computational efficiency, of the average PER in block-fading channels. The numerical analysis shows that the approximation holds tight for any value of SNR and packet length whereas the earlier studies approximate the average PER only at asymptotic SNRs and packet lengths.
Polynomial-time algorithm for determining the graph isomorphism (v.2)
Venue: arXiv cs.DS 2016
Year: 2016

We develop the methodology of positioning graph vertices relative to each other to solve the problem of determining isomorphism of two undirected graphs. Based on the position of the vertex in one of the graphs, it is determined the corresponding vertex in the other graph. For the selected vertex of the undirected graph, we define the neighborhoods of the vertices. Next, we construct the auxiliary directed graph, spawned by the selected vertex. The vertices of the digraph are positioned by special characteristics --- vectors, which locate each vertex of the digraph relative the found neighborhoods. This enabled to develop the algorithm for determining graph isomorphism, the runing time of which is equal to $O(n^4)$.
Decision Tree Classification on Outsourced Data
Venue: arXiv cs.LG 2016
Year: 2016

This paper proposes a client-server decision tree learning method for outsourced private data. The privacy model is anatomization/fragmentation: the server sees data values, but the link between sensitive and identifying information is encrypted with a key known only to clients. Clients have limited processing and storage capability. Both sensitive and identifying information thus are stored on the server. The approach presented also retains most processing at the server, and client-side processing is amortized over predictions made by the clients. Experiments on various datasets show that the method produces decision trees approaching the accuracy of a non-private decision tree, while substantially reducing the client's computing resource requirements.
Statistical Learning Theory Approach for Data Classification with l-diversity
Venue: arXiv cs.LG 2016
Year: 2016

Corporations are retaining ever-larger corpuses of personal data; the frequency or breaches and corresponding privacy impact have been rising accordingly. One way to mitigate this risk is through use of anonymized data, limiting the exposure of individual data to only where it is absolutely needed. This would seem particularly appropriate for data mining, where the goal is generalizable knowledge rather than data on specific individuals. In practice, corporate data miners often insist on original data, for fear that they might "miss something" with anonymized or differentially private approaches. This paper provides a theoretical justification for the use of anonymized data. Specifically, we show that a support vector classifier trained on anatomized data satisfying l-diversity should be expected to do as well as on the original data. Anatomy preserves all data values, but introduces uncertainty in the mapping between identifying and sensitive values, thus satisfying l-diversity. The theoretical effectiveness of the proposed approach is validated using several publicly available datasets, showing that we outperform the state of the art for support vector classification using training data protected by k-anonymity, and are comparable to learning on the original data.
Robot Vision Architecture for Autonomous Clothes Manipulation
Venue: arXiv cs.RO 2016
Year: 2016

This paper presents a novel robot vision architecture for perceiving generic 3D clothes configurations. Our architecture is hierarchically structured, starting from low-level curvatures, across mid-level geometric shapes \& topology descriptions; and finally approaching high-level semantic surface structure descriptions. We demonstrate our robot vision architecture in a customised dual-arm industrial robot with our self-designed, off-the-self stereo vision system, carrying out autonomous grasping and dual-arm flattening. It is worth noting that the proposed dual-arm flattening approach is unique among the state-of-the-art robot autonomous system, which is the major contribution of this paper. The experimental results show that the proposed dual-arm flattening using stereo vision system remarkably outperforms the single-arm flattening and widely-cited Kinect-based sensing system for dexterous manipulation tasks. In addition, the proposed grasping approach achieves satisfactory performance on grasping various kind of garments, verifying the capability of proposed visual perception architecture to be adapted to more than one clothing manipulation tasks.
NP-hard sets are not sparse unless P=NP: An exposition of a simple proof of Mahaney's Theorem, with applications
Venue: arXiv cs.CC 2016
Year: 2016

Mahaney's Theorem states that, assuming $\mathsf{P} \neq \mathsf{NP}$, no NP-hard set can have a polynomially bounded number of yes-instances at each input length. We give an exposition of a very simple unpublished proof of Manindra Agrawal whose ideas appear in Agrawal-Arvind ("Geometric sets of low information content," Theoret. Comp. Sci., 1996). This proof is so simple that it can easily be taught to undergraduates or a general graduate CS audience - not just theorists! - in about 10 minutes, which the author has done successfully several times. We also include applications of Mahaney's Theorem to fundamental questions that bright undergraduates would ask which could be used to fill the remaining hour of a lecture, as well as an application (due to Ikenmeyer, Mulmuley, and Walter, arXiv:1507.02955) to the representation theory of the symmetric group and the Geometric Complexity Theory Program. To this author, the fact that sparsity results on NP-complete sets have an application to classical questions in representation theory says that they are not only a gem of classical theoretical computer science, but indeed a gem of mathematics.
Centrality measures in networks based on nodes attributes, long-range interactions and group influence
Venue: arXiv cs.SI 2016
Year: 2016

We propose a new method for assessing agents influence in network structures, which takes into consideration nodes attributes, individual and group influences of nodes, and the intensity of interactions. This approach helps us to identify both explicit and hidden central elements which cannot be detected by classical centrality measures or other indices.
From Malware Signatures to Anti-Virus Assisted Attacks
Venue: arXiv cs.CR 2016
Year: 2016

Although anti-virus software has significantly evolved over the last decade, classic signature matching based on byte patterns is still a prevalent concept for identifying security threats. Anti-virus signatures are a simple and fast detection mechanism that can complement more sophisticated analysis strategies. However, if signatures are not designed with care, they can turn from a defensive mechanism into an instrument of attack. In this paper, we present a novel method for automatically deriving signatures from anti-virus software and demonstrate how the extracted signatures can be used to attack sensible data with the aid of the virus scanner itself. We study the practicability of our approach using four commercial products and exemplarily discuss a novel attack vector made possible by insufficiently designed signatures. Our research indicates that there is an urgent need to improve pattern-based signatures if used in anti-virus software and to pursue alternative detection approaches in such products.
Portable Ontological Expressions in NoSQL Queries
Venue: arXiv cs.DB 2016
Year: 2016

A significant barrier to the portability of queries across di- verse physical implementations of large data stores, espe- cially NoSQL data stores, is that the queries reference the physical storage attributes, such as the table and column names. In this paper, we describe a technique for embed- ding ontological expressions called Address Expressions, or A-Expressions, in NoSQL queries to improve their portability across diverse physical implementations. We discuss an implementation of such queries over a MongoDB data store of the Enron email corpus with examples, and conduct a preliminary performance assessment.
Finding Clustering Configurations to Accurately Infer Packet Structures from Network Data
Venue: arXiv cs.NI 2016
Year: 2016

Clustering is often used for reverse engineering network protocols from captured network traces. The performance of clustering techniques is often contingent upon the selection of various parameters, which can have a severe impact on clustering quality. In this paper we experimentally investigate the effect of four different parameters with respect to network traces. We also determining the optimal parameter configuration with respect to traces from four different network protocols. Our results indicate that the choice of distance measure and the length of the message has the most substantial impact on cluster accuracy. Depending on the type of protocol, the $n$-gram length can also have a substantial impact.
POI: Multiple Object Tracking with High Performance Detection and Appearance Feature
Venue: arXiv cs.CV 2016
Year: 2016

Detection and learning based appearance feature play the central role in data association based multiple object tracking (MOT), but most recent MOT works usually ignore them and only focus on the hand-crafted feature and association algorithms. In this paper, we explore the high-performance detection and deep learning based appearance feature, and show that they lead to significantly better MOT results in both online and offline setting. We make our detection and appearance feature publicly available. In the following part, we first summarize the detection and appearance feature, and then introduce our tracker named Person of Interest (POI), which has both online and offline version.
Dynamical Behavior Investigation and Analysis of Novel Mechanism for Simulated Spherical Robot named "RollRoller"
Venue: arXiv cs.RO 2016
Year: 2016

This paper introduces a simulation study of fluid actuated multi-driven closed system as spherical mobile robot called "RollRoller". Robot's mechanism design consists of two essential parts: tubes to lead a core and mechanical controlling parts to correspond movements. Our robot gets its motivation force by displacing the spherical movable mass known as core in curvy manners inside certain pipes. This simulation investigates by explaining the mechanical and structural features of the robot for creating hydraulic-base actuation via force and momentum analysis. Next, we categorize difficult and integrated 2D motions to omit unstable equilibrium points through derived nonlinear dynamics. We propose an algorithmic position control in forward direction that creates hybrid model as solution for motion planning problem in spherical robot. By deriving nonlinear dynamics of the spherical robot and implementing designed motion planning, we show how RollRoller can be efficient in high speed movements in comparison to the other pendulum-driven models. Then, we validate the results of this position control obtained by nonlinear dynamics via Adams/view simulation which uses the imported solid model of RollRoller. Lastly, We have a look to the circular maneuver of this robot by the same simulator.
Finding Online Extremists in Social Networks
Venue: arXiv cs.SI 2016
Year: 2016

Online extremists in social networks pose a new form of threat to the general public. These extremists range from cyberbullies who harass innocent users to terrorist organizations such as the Islamic State of Iraq and Syria (ISIS) that use social networks to recruit and incite violence. Currently social networks suspend the accounts of such extremists in response to user complaints. The challenge is that these extremist users simply create new accounts and continue their activities. In this work we present a new set of operational capabilities to deal with the threat posed by online extremists in social networks. Using data from several hundred thousand extremist accounts on Twitter, we develop a behavioral model for these users, in particular what their accounts look like and who they connect with. This model is used to identify new extremist accounts by predicting if they will be suspended for extremist activity. We also use this model to track existing extremist users as they create new accounts by identifying if two accounts belong to the same user. Finally, we present a model for searching the social network to efficiently find suspended users' new accounts based on a variant of the classic Polya's urn setup. We find a simple characterization of the optimal search policy for this model under fairly general conditions. Our urn model and main theoretical results generalize easily to search problems in other fields.
Adaptive Substring Extraction and Modified Local NBNN Scoring for Binary Feature-based Local Mobile Visual Search without False Positives
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, we propose a stand-alone mobile visual search system based on binary features and the bag-of-visual words framework. The contribution of this study is three-fold: (1) We propose an adaptive substring extraction method that adaptively extracts informative bits from the original binary vector and stores them in the inverted index. These substrings are used to refine visual word-based matching. (2) A modified local NBNN scoring method is proposed in the context of image retrieval, which considers the density of binary features in scoring each feature matching. (3) In order to suppress false positives, we introduce a convexity check step that imposes a convexity constraint on the configuration of a transformed reference image. The proposed system improves retrieval accuracy by 11% compared with a conventional method without increasing the database size. Furthermore, our system with the convexity check does not lead to false positive results.
Online Training of an Opto-Electronic Reservoir Computer Applied to Real-Time Channel Equalisation
Venue: arXiv cs.ET 2016
Year: 2016

Reservoir Computing is a bio-inspired computing paradigm for processing time dependent signals. The performance of its analogue implementation are comparable to other state of the art algorithms for tasks such as speech recognition or chaotic time series prediction, but these are often constrained by the offline training methods commonly employed. Here we investigated the online learning approach by training an opto-electronic reservoir computer using a simple gradient descent algorithm, programmed on an FPGA chip. Our system was applied to wireless communications, a quickly growing domain with an increasing demand for fast analogue devices to equalise the nonlinear distorted channels. We report error rates up to two orders of magnitude lower than previous implementations on this task. We show that our system is particularly well-suited for realistic channel equalisation by testing it on a drifting and a switching channels and obtaining good performances
Safeguarding Decentralized Wireless Networks Using Full-Duplex Jamming Receivers
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we study the benefits of full-duplex (FD) receiver jamming in enhancing the physical-layer security of a two-tier decentralized wireless network with each tier deployed with a large number of pairs of a single-antenna transmitter and a multi-antenna receiver. In the underlying tier, the transmitter sends unclassified information, and the receiver works in the halfduplex (HD) mode receiving the desired signal. In the overlaid tier, the transmitter deliveries confidential information in the presence of randomly located eavesdroppers, and the receiver works in the FD mode radiating jamming signals to confuse eavesdroppers and receiving the desired signal simultaneously. We provide a comprehensive performance analysis and network design under a stochastic geometry framework. Specifically, we consider the scenarios where each FD receiver uses single- and multi-antenna jamming, and analyze the connection probability and the secrecy outage probability of a typical FD receiver by providing accurate expressions and more tractable approximations for the two metrics. We further determine the optimal deployment of the FD-mode tier in order to maximize networkwide secrecy throughput subject to constraints including the given dual probabilities and the network-wide throughput of the HD-mode tier. Numerical results are demonstrated to verify our theoretical findings, and show that network-wide secrecy throughput is significantly improved by properly deploying the FD-mode tier.